Batch samples of the grains or seeds of some crop plants, such as corn, are often measured and/or evaluated using a sensor device, such as a near-infrared (NIR) spectrometer for example. As one skilled in the art will appreciate, such sensor devices may be capable of evaluating seed or grain traits that are discernible by scanning an outer surface of each seed or grain. Furthermore, there are a number of different traits for which the accurate measurement of all kernels in a sample and a given seed or grain in a sample and in several spatial configurations (i.e. at several points on the surface of each seed) may be important.
For example, the oil content in a sample of corn kernels is distributed substantially homogeneously within the sample. However oil content is only detectable on the germ side of any one particular corn kernel, such that if different sides and/or spatial configurations of each kernel are presented to a sensor device, different oil content results are obtained. Thus, in order to assess oil content in a sample of corn kernels, one must compute an average of oil content measurements obtained over the sample after each kernel has been presented to the sensor device in several different spatial configurations.
In another example, amino acid content in particulate samples (such as corn kernels) is generally present in a low concentration across the sample, such that for a scan of the sample, the signal-to-noise ratio is relatively low. By presenting several surfaces of the kernels in the sample to the sensor device and obtaining an average of the scans of different surfaces (corresponding to different spatial configurations of each kernel), one may substantially increase the signal-to-noise ratio and increase the overall accuracy and precision of amino acid measurements in such a sample. The same principle holds for traits such as “high total fermentables” (HTF) in corn kernel samples without well defined chemical absorption patterns.
In another example, the presence of mold and/or mycotoxins in a particulate sample (such as a batch of corn kernels, for example) may be substantially heterogeneous within the sample (i.e. there are high detectable levels in some kernels and low or non-existent in other kernels). Furthermore, the number of kernels affected to a measurable extent by mold or mycotoxin may be relatively small (even in a large sample size). Thus, in order to detect and/or assess the extent of mold and/or mycotoxin effects in a corn kernel sample, there exists a need to obtain an average measurement across the sample. There further exists a need for a measurement system that may be capable of accepting large sample sizes (to ensure that even small numbers of mold-affected kernels are detected).
In summary, there are a number of different traits for which the accurate measurement of all or most of the seeds or grains in a given sample at several points on the surface of each seed or grain is important. For example, accurate measurement of all the seeds or grains in a sample (at several points on the surface of each seed or grain) may be important for evaluating traits with non-random distribution, low concentration in most seeds or grains, and/or very high concentration in some seeds or grains.
Conventional methods of subjecting several surfaces of each seed or grain within a sample to measurement by an adjacent sensor device involve manually shaking and/or manipulating the seed sample. For example, a technician may scan a small sample of seed or grain using a sensor device, then shake and/or otherwise agitate the sample between scans to rotate or tumble individual seeds within the sample, and finally re-scan the sample to obtain scans of alternate points on the surface of various seeds or grains within the sample. To obtain a statistically significant and/or substantially complete set of sensor measurements for a given sample, the manual rotation/shaking/agitation process is typically repeated many times. Thus, conventional methods for evaluating seed or grain samples using sensor measurements may be very time consuming, result in generally low throughput, and may often lead to incomplete sampling and/or flawed data. Furthermore, conventional methods for mixing samples such that each kernel and/or seed within the sample is presented to a sensor device in several spatial configurations do not allow for the accommodation of different and/or very large sample sizes. Thus, conventional mixing and/or presentation methods may not allow for the detection of traits that are present only in a few representative particles of a relatively large sample (having hundreds or thousands of particles, for example).
Therefore, in order to facilitate faster and substantially complete scanning of a sample of seeds or grains (using a sensor such as an NIR spectrometer for example) in several spatial configurations there exists a need in the art for an apparatus that effectively and systematically presents several surfaces of individual seeds or grains within a sample to a scanning area spanned by a sensor device. There also exists a need for a system, device, and method that allows for the fast, accurate, and complete measurement of the traits of substantially all (or at least a statistically-significant sample thereof) of the seeds or grains in a sample while continuously adjusting the spatial configurations of each seed or grain in the scanning area. There further exists a need for a system, device, and method satisfying the needs listed above that accommodates variable and/or relatively large sample sizes such that relatively rare traits may be accurately identified and/or quantified within a sample.